Radiographic-deformation and textural heterogeneity (r-DepTH): an integrated descriptor for brain tumor prognosis

ABSTRACT

Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/694,167 filed Jul. 5, 2018, the contents of which areherein incorporated by reference in their entirety.

FEDERAL FUNDING NOTICE

This invention was made with government support under grants1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R21CA179327,R21CA195152-01, R01DK098503-02, and 1 C06 RR012463-01 awarded by theNational Institutes of Health. Also grants W81XWH-13-1-0418 andW81XWH-14-1-0323 awarded by the Department of Defense. The governmenthas certain rights in the invention.

BACKGROUND

Most aggressing tumors are systemic, implying that their impact is notlocalized to the tumor itself but extends well beyond the visible tumorborders. For instance, solid tumors (e.g., Glioblastoma) typically exertpressure on the surrounding normal parenchyma due to activeproliferation, impacting neighboring structures and worsening survival.Existing approaches to predicting overall survival (OS) in Glioblastoma(GBM) have focused on capturing tumor heterogeneity via shape,intensity, and texture radiomic statistics within the visible surgicalmargins on pre-treatment scans, with the clinical purpose of improvingtreatment management. However, a poorly understood aspect ofheterogeneity is the impact of active proliferation and tumor burdenthat may lead to subtle deformations in the surrounding normalparenchyma distal to the tumor.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example operations,apparatus, methods, and other example embodiments of various aspectsdiscussed herein. It will be appreciated that the illustrated elementboundaries (e.g., boxes, groups of boxes, or other shapes) in thefigures represent one example of the boundaries. One of ordinary skillin the art will appreciate that, in some examples, one element can bedesigned as multiple elements or that multiple elements can be designedas one element. In some examples, an element shown as an internalcomponent of another element may be implemented as an external componentand vice versa. Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates textural feature differences between tumors from twodifferent Glioblastoma (GBM) patients experiencing short-term survival(STS) and long-term survival (LTS) respectively.

FIG. 2 illustrates a workflow diagram of an example method or set ofoperations that employs a machine learning classifier to distinguish LTSfrom STS in a GBM patient according to various embodiments discussedherein.

FIGS. 3A and 3B illustrate an example tumoral region and peritumoralregion.

FIG. 4 illustrates a workflow diagram of an example method or set ofoperations that employs a machine learning classifier to distinguish LTSfrom STS in a GBM patient according to various embodiments discussedherein.

FIG. 5 illustrates a flow diagram of an example method or set ofoperations for training a machine learning classifier according tovarious embodiments discussed herein.

FIG. 6 illustrates a workflow diagram of an example method or set ofoperations that employs a machine learning classifier to distinguish LTSfrom STS in a GBM patient according to various embodiments discussedherein.

FIG. 7 illustrates T1w magnetic resonance imaging (MRI) scans of STS andLTS GBM patients.

FIG. 8 illustrates Kaplan-Meier (KM) curves obtained according tovarious embodiments discussed herein.

FIG. 9 illustrates a table describing features computed from T1w scansto distinguish GBM LTS from STS according to various embodimentsdiscussed herein.

FIG. 10 illustrates a diagram of an example apparatus that canfacilitate distinguishing LTS from STS in a GBM patient according tovarious embodiments discussed herein.

FIG. 11 illustrates a diagram of an example apparatus that canfacilitate distinguishing LTS from STS in a GBM according to variousembodiments discussed herein.

FIG. 12 illustrates a diagram of an example computer in whichembodiments described herein may be implemented.

DETAILED DESCRIPTION

Cancer is not a bounded, self-organized system. Most malignant tumorshave heterogeneous growth, leading to disorderly proliferation wellbeyond the surgical margins. In solid tumors, depending on the malignantphenotype, the impact of the tumor is observed not just within thevisible tumor, but also in the immediate peritumoral, as well as in theseemingly normal-appearing adjacent field. The phenomenon of tumorinvolvement outside of the visible surgical margins is known as “tumorfield effect”. Existing approaches to predicting overall survival (OS)in Glioblastoma (GBM) leave unexplored tumor field effect impact on OS,where such impact is caused by the pressure exerted on the surroundingnormal parenchyma caused by active proliferation and tumor burdenthereof. For instance, in GBM, the herniation or gross distortion of thebrainstem (remote to the tumor location) may be the proximal cause ofdeath in 60% of GBM patients.

Radiomic features extracted from a tumoral region on radiographicimagery, including magnetic resonance imaging (MRI) imagery or computedtomography (CT) imagery may be employed for capturing intra-tumoralheterogeneity, which may be employed to generate a prognosis of OS.Radiomic features extracted from peritumoral regions in GBM may also beprognostic of OS. Similarly, the tumor field effect in GBM may bemanifested several millimeters distal to the visible tumor margins.Radiomics includes the computerized extraction of and analysis ofsub-visual attributes from radiographic imagery (e.g., MRI, CT), and thequantification of phenotypic characteristics of a region of interest(ROI) (e.g., lesion, tumor) represented in the imagery based on theextracted features. Embodiments mine or extract prognostic informationfrom the subtle deformations due to tumor proliferation and burden inthe seemingly normal parenchyma distal to tumor boundaries. Embodimentscombine these extra-tumoral deformations or statistical measurescomputed based on the extra-tumoral deformations, with textural patternsor statistical measures of textural patterns, extracted from within thetumor confines and from the peritumoral region, into an integrateddescriptor (r-DepTH) of radiographic deformation and texturalheterogeneity to facilitate a more comprehensive characterization oftumor heterogeneity than existing approaches. Embodiments may employ theintegrated descriptor (r-DepTH) as a prognostic marker to more reliablypredict patient survival in solid tumors.

Embodiments employing the r-DepTH descriptor capture heterogeneity insolid tumors from both the intra-tumoral region and peritumoral region,and the extra-tumoral field. Highly aggressive solid tumors having worseoutcome may proliferate in a more disorderly fashion, and hence lead tomore heterogeneous deformations in the surrounding normal parenchyma,and to higher textural heterogeneity within the tumor confines, ascompared to relatively less aggressive tumors with overall improvedoutcomes. Embodiments capture textural heterogeneity from the tumoralregion (

_(tex) ^(T)) and textural heterogeneity from the peritumoral regions (

_(tex) ^(P)) using, in one embodiment, co-occurrence of localanisotropic gradient orientations (CoLIAGe) features. Embodimentsfurther capture deformation heterogeneity (

_(def)) within the normal parenchyma as a function of the distance fromthe tumor margins. The r-DepTH descriptor is then obtained as

_(depth)=[

_(tex) ^(F),

_(tex) ^(P),

_(def)]. FIG. 1 illustrates radiomic features associated with a GBMtumor for two different patients, one patient with STS at 110, andanother with LTS at 120. Tumor regions are illustrated for the STSpatient at 112 and the LST patient at 122. Textural differences withinthe tumoral regions 112 and 122 are illustrated at 114 for the STSpatient and at 124 for the LTS patient, respectively. Correspondingdeformation magnitudes in the surrounding normal parenchyma areillustrated at 116 for the STS patient and at 126 for the LTS patient,respectively. A smaller region outside the tumor across the STS patientand LTS patient is illustrated for the STS patient at 118 and the LTSpatient at 128.

Embodiments extract radiomic features that are predictive of long-term(LTS) GBM survival versus short-term (STS) GBM survival fromradiographic imagery, including MRI imagery or CT imagery, and generatea prognostic prediction of outcome for the patient of whom the imageryis associated, based on the radiomic features, that is significantlyimproved compared to existing approaches that may only employdeformation alone or texture features alone. Embodiments furtherfacilitate identifying GBM patients who would receive added benefit froma first course of therapy or a second, different course of therapy, andfurther facilitate improved treatment management in solid tumors,compared to existing approaches that may only employ deformation aloneor texture features alone.

Embodiments described herein can employ techniques discussed herein fordistinguishing LTS from STS via a machine learning classifier trained onradiological imagery (e.g., MRI, CT) and radiomic features anddeformation heterogeneity extracted from said imagery that have beenidentified as distinguishing between lesions (e.g., tumors) associatedwith different survival times. In various embodiments, radiomic featuresand deformation heterogeneity employed by various embodiments mayinclude intratumoral and peritumoral radiomic features. Embodiments mayemploy intratumoral and peritumoral radiomic features and deformationheterogeneity that quantify heterogeneity patterns from the region ofinterest as an independent predictor of survival time.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic or circuit, and so on.The physical manipulations create a concrete, tangible, useful,real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, calculating, determining, and so on, refer to actions andprocesses of a computer system, logic, circuit, processor, or similarelectronic device that manipulates and transforms data represented asphysical (electronic) quantities.

Example methods and operations may be better appreciated with referenceto flow diagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

Various embodiments can employ techniques discussed herein to facilitatedistinguishing LTS from STS in GBM patients. FIG. 2 illustrates a flowdiagram of an example method or set of operations 200 that employs amachine learning classifier to distinguish LTS from STS in GBM patients,according to various embodiments discussed herein. A processor(s) mayinclude any combination of general-purpose processors and dedicatedprocessors (e.g., graphics processors, application processors, etc.).The processors may be coupled with or may include memory or storage andmay be configured to execute instructions stored in the memory orstorage to enable various apparatus, applications, or operating systemsto perform the operations or methods described herein. The memory orstorage devices may include main memory, disk storage, or any suitablecombination thereof. The memory or storage devices may include, but arenot limited to any type of volatile or non-volatile memory such asdynamic random access memory (DRAM), static random-access memory (SRAM),erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), Flash memory, or solid-statestorage.

The method or set of operations 200 includes, at 210, accessing aradiological image associated with a patient. The radiological imageincludes a region of interest (ROI) demonstrating Glioblastoma (GBM)pathology. The radiological image has a plurality of pixels, a pixelhaving an intensity. The radiological image may have a plurality ofvoxels, a voxel having an intensity. The radiological image includes arepresentation of a tumoral region. In one embodiment, the radiologicalimage is a T1w MRI image. In one embodiment, the radiological image is a3-Tesla (3 T) treatment-naïve Gadolinium (Gd)-contrast T1w image, a 3 Ttreatment-naïve T2w Gd-contrast image, or a FLAIR MRI image. Theaccessed radiological image (e.g., T1w MRI image, T2w Gd-contrast image,FLAIR MRI image) can be stored in memory locally or remotely, and can beobtained via a medical imaging device one of concurrently with method oroperations 200 (e.g., via a medical imaging device implementing methodor operations 200) or prior to method or operations 200. Accessing theradiological image (e.g., T1w MRI image) includes acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

The set of operations 200 also includes, at 220, segmenting the tumoralregion represented in the image. In one embodiment, the tumoral regionis segmented using a watershed segmentation technique, a region growingor active contour technique, or a convolutional neural network (CNN)approach. Segmenting the tumoral region includes defining a tumoralboundary. In one embodiment, the tumoral region may be segmented one ofconcurrently with method or operations 200 (e.g., via a medical imagingdevice implementing method 200) or prior to method or operations 200.Segmenting the tumoral region includes acquiring electronic data,reading from a computer file, receiving a computer file, reading from acomputer memory, or other computerized activity not practicallyperformed in the human mind.

The set of operations 200 also includes, at 222, defining a peritumoralregion represented in the image based on the tumoral region. In oneembodiment, the peritumoral region is defined based on the tumoralboundary. In one embodiment, defining the peritumoral region includesperforming a dilation of the tumoral boundary. The peritumoral regionmay include a plurality of annular rings. In one embodiment, performinga dilation of the tumoral boundary includes dilating the tumoralboundary. In one embodiment, the peritumoral region is defined based ona region of edema represented in the FLAIR MRI image. In thisembodiment, the peritumoral region extends 65 mm from the tumoralboundary. In another embodiment, performing a dilation of the tumoralboundary includes dilating the tumoral boundary another, differentamount (e.g., 9 mm, 15 mm). In one embodiment, all three sequences(e.g., Gd-T1w, T2w, and FLAIR image) are used in to obtain the tumoraland peritumoral region segmentations. In this embodiment, the Gd-T1wimage highlights the enhancing tumor region in the scan. In thisembodiment, the T2w and FLAIR images are used to delineate theperitumoral edema boundaries. Defining the peritumoral region includesacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind.

FIG. 3A illustrates an example tumoral region 310. A tumor representedin a radiological image as described herein has a tumoral boundary.Embodiments define a peritumoral region based on a morphologicaltransformation of the tumoral boundary. A peritumoral region may bedefined as the region surrounding the tumoral region out to a distance.For example, in one embodiment, the peritumoral region may be the regionextending 2 mm from the tumoral boundary. In another embodiment, theperitumoral region may be the region extending 6 mm from the tumoralboundary, 12 mm from the tumoral boundary, or 65 mm from the tumoralboundary. The peritumoral region may be defined by a distance measuredin mm, as described, or in other units, including pixels.

FIG. 3A illustrates an example peritumoral region 340 associated with aGBM lesion or tumoral region 310. Peritumoral region 340 is bounded byouter peritumoral boundary 330 and tumoral boundary 320. In oneembodiment, example operations, methods, and apparatus morphologicallydilate tumoral boundary 320 by an amount 350, resulting in the outerperitumoral boundary 330. Amount 350 may be, for example, 2 mm, 4 mm, 6mm, 65 mm, 6 pixels, 12 pixels, or another, different amount. FIG. 3Billustrates an example peritumoral region that includes four annularrings 371, 372, 373, and 375 defined from the peritumoral boundary 320.Annular ring 371 extends from 0 mm to 3 mm from the tumoral boundary.Annular ring 372 extends from 3 mm to 6 mm from the tumoral boundary.Annular ring 373 extends from 6 mm to 9 mm from the tumoral boundary.Annular ring 375 extends from 9 mm to 12 mm from the tumoral boundary.In another embodiment, other annular ring sizes, radii, numbers of bandsor rings, or techniques may be employed to define the peritumoralregion.

In another embodiment, the peritumoral boundary may be generated usingother techniques. For example, the peritumoral boundary may be definedas a function of a property of the tumor. The property of the tumor mayinclude, for example, a diameter, a radius, a perimeter, an area, avolume, or other property of the tumor. The function may define theperitumoral region as, for example, a morphologic dilation of thetumoral boundary, where the dilation ratio is defined by a magnitude ofan axis of the tumor. In another embodiment, the peritumoral boundarymay be defined as a disc of a threshold radius defined about thecentroid of the tumor, or defined on the focal points of an ellipticalrepresentation of the tumor. In one embodiment, the peritumoral boundarymay be manually defined. Other approaches or combinations of approachesmay be used to define the peritumoral boundary. Defining the peritumoralregion includes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

Returning to FIG. 2 , the set of operations 200 also includes, at 224,defining a parenchymal region represented in the image. In oneembodiment, the parenchymal region includes a plurality of annularsub-regions. In one embodiment, each member of the plurality of annularsub-regions is a 5 mm annular sub-region. In another embodiment, eachmember of the plurality of annular sub-regions may have another,different width (e.g., 4 mm, 6 mm, 10 mm). Defining the parenchymalregion includes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

The set of operations 200 also includes, at 230, computing a deformationheterogeneity feature descriptor based on the parenchymal region.Computing the deformation heterogeneity feature descriptor includesaccessing a healthy brain atlas. A healthy brain atlas is constructed byregistering healthy brain imagery to a common coordinate space andtaking a voxel-wise average of the intensities of the voxels. Computingthe deformation heterogeneity feature descriptor also includesregistering the parenchymal region to the healthy brain atlas. In oneembodiment, registering the parenchymal region to the healthy brainatlas includes registering the parenchymal region to the healthy brainatlas using a non-rigid mutual information based similarity measureregistration approach. Computing the deformation heterogeneity featuredescriptor further includes computing the deformation heterogeneityfeature descriptor based on the registration of the parenchymal regionwith the healthy brain atlas. In one embodiment, the deformationheterogeneity feature descriptor is computed based on first orderstatistics computed from a deformation magnitude of each voxel of eachof the plurality of annular sub-regions, respectively. In oneembodiment, the deformation heterogeneity feature descriptor is computedsuch that only spatial differences due to structural deformation causedby mass effect are recovered when compared to the healthy brain atlas.In one embodiment, the deformation heterogeneity feature descriptor maybe represented as

_(def). Computing the deformation heterogeneity feature descriptorincludes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

The set of operations 200 also includes, at 232, computing a tumoralthree-dimensional (3D) gradient-based texture descriptor based on thesegmented tumoral region. In one embodiment, the tumoral 3Dgradient-based texture descriptor includes a co-occurrence of localanisotropic gradient orientations (CoLlaGe) feature. CoLlaGE featurescapture differences between benign and pathologic phenotypes which maybe visually indistinguishable on routine anatomic imaging. CoLIAGefeatures capture and exploit local anisotropic differences invoxel-level gradient orientations to distinguish similar appearingphenotypes. Generating a CoLIAGe feature includes assigning every imagevoxel an entropy value associated with the co-occurrence matrix ofgradient orientations computed around every voxel. In one embodiment,the tumoral 3D gradient-based texture descriptor includes five firstorder statistics of entropy, energy, inertia, IDM, correlation, Info1,Info2, sum average, sum variance, sum entropy, difference average,difference variance, and differential entropy. In various embodiments,the tumoral 3D gradient-based texture descriptor may include N (N beinga positive integer, e.g., 5, or a greater or lesser number) radiomicfeatures that have been identified (e.g., via an algorithm or measuresuch as sequential forward feature selection, Pearson's correlationcoefficient, minimum redundancy maximum relevance (mRMR), Wilcoxon ranksum, etc.) as the N most distinguishing or discriminating radiomicfeatures for distinguishing a first class (e.g., LTS) from a second,different class (e.g., STS). In one embodiment, the tumoral 3Dgradient-based texture descriptor may be represented as

_(tex) ^(T). Computing the tumoral 3D gradient-based texture descriptorincludes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

The set of operations 200 also includes, at 234, computing a peritumoral3D gradient-based textural descriptor based on the peritumoral region.In one embodiment, the peritumoral 3D gradient-based textural descriptorincludes a CoLlaGe feature. In one embodiment, the peritumoral 3Dgradient-based texture descriptor includes five first order statisticsof entropy, energy, inertia, IDM, correlation, Info1, Info2, sumaverage, sum variance, sum entropy, difference average, differencevariance, and differential entropy. In various embodiments, theperitumoral 3D gradient-based texture descriptor may include N (N beinga positive integer, e.g., 5, or a greater or lesser number) radiomicfeatures that have been identified (e.g., via an algorithm or measuresuch as sequential forward feature selection, Pearson's correlationcoefficient, minimum redundancy maximum relevance (mRMR), Wilcoxon ranksum, etc.) as the N most distinguishing or discriminating radiomicfeatures for distinguishing a first class (e.g., LTS) from a second,different class (e.g., STS). In one embodiment, the peritumoral 3Dgradient-based textural descriptor may be represented as

_(tex) ^(P). Computing the peritumoral 3D gradient-based texturaldescriptor includes acquiring electronic data, reading from a computerfile, receiving a computer file, reading from a computer memory, orother computerized activity not practically performed in the human mind.

The set of operations 200 also includes, at 236, generating aradiographic-deformation and textural heterogeneity (r-DepTH)descriptor. The r-DepTH descriptor is based on the deformationheterogeneity feature descriptor, the tumoral 3D gradient-based texturaldescriptor, and the peritumoral 3D gradient-based textural descriptor.In one embodiment, the r-DepTH descriptor may be represented as

_(depth)=[

_(tex) ^(T),

_(tex) ^(P),

_(def)]. Generating the r-DepTH descriptor includes acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

The set of operations 200 also includes, at 240, providing the r-DepTHdescriptor to a machine learning classifier trained to distinguishlong-term survival (LTS) from short-term survival (STS) in GBM based onthe r-DepTH descriptor. In one embodiment, the machine learningclassifier is a linear discriminant analysis (LDA) classifier. Inanother embodiment, the machine learning classifier may be another,different type of machine learning classifier, for example, a quadraticdiscriminant analysis (QDA) classifier, a support vector machine (SVM)classifier, a random forests (RF) classifier, or a deep learningclassifier, including a convolutional neural network (CNN). Providingthe r-Depth descriptor to the machine learning classifier includesacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind.

The set of operations 200 also includes, at 250, receiving, from themachine learning classifier, a probability that the patient willexperience LTS. The machine learning classifier computes the probabilitybased on the r-Depth descriptor. Receiving the probability from themachine learning classifier includes acquiring electronic data, readingfrom a computer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

The set of operations 200 also includes, at 260, generating aclassification of the patient as likely to experience LTS or as likelyto experience STS based, at least in part, on the probability. Invarious embodiments, the classification may include one or more of amost likely outcome (e.g., as determined based on the probability basedon the r-DepTH descriptor, etc.) such as LTS; a probability orconfidence associated with a most likely outcome; and/or associatedprobabilities/confidences associated with each of a plurality ofoutcomes (e.g., LTS, STS). For example, in one embodiment, generatingthe classification includes classifying the patient associated with theROI as LTS when the probability is >=0.5, or classifying the patient asSTS when the probability is <0.5. In this embodiment, a classificationof LTS corresponds with an overall survival (OS)>540 days, while aclassification of STS corresponds with an OS<240 days. In anotherembodiment, other classification schemes may be employed. In oneembodiment, the classification is generated with an AUC of at least0.83, with a KM curve analysis in which p=0.038. Generating theclassification includes acquiring electronic data, reading from acomputer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

The set of operations 200 further includes, at 270, displaying theclassification. In one embodiment, the set of operations 200 includes,at 270, displaying the classification and optionally displaying one ormore of the image, the r-DepTH descriptor, the tumoral 3D gradient-basedtextural descriptor, the peritumoral 3D gradient-based texturaldescriptor, or the probability. Displaying the classification andoptionally displaying one or more of the image, the r-DepTH descriptor,the tumoral 3D gradient-based textural descriptor, the peritumoral 3Dgradient-based textural descriptor, or the probability may includedisplaying the classification and optionally displaying one or more ofthe image, the r-DepTH descriptor, the tumoral 3D gradient-basedtextural descriptor, the peritumoral 3D gradient-based texturaldescriptor, or the probability on a computer monitor, a smartphonedisplay, a tablet display, or other displays. Displaying theclassification and optionally displaying one or more of the image, ther-DepTH descriptor, the tumoral 3D gradient-based textural descriptor,the peritumoral 3D gradient-based textural descriptor, or theprobability can also include printing the classification and optionallydisplaying one or more of the image, the r-DepTH descriptor, the tumoral3D gradient-based textural descriptor, the peritumoral 3D gradient-basedtextural descriptor, or the probability. Displaying the classificationand optionally displaying one or more of the image, the r-DepTHdescriptor, the tumoral 3D gradient-based textural descriptor, theperitumoral 3D gradient-based textural descriptor, or the probabilitycan also include controlling a GBM survival prediction system, apersonalized medicine system, a monitor, or other display, to displayoperating parameters or characteristics of a machine learningclassifier, during at least one of training and testing of the machinelearning classifier, or during clinical operation of the machinelearning classifier. By displaying the classification and optionallydisplaying one or more of the image, the r-DepTH descriptor, the tumoral3D gradient-based textural descriptor, the peritumoral 3D gradient-basedtextural descriptor, or the probability, example embodiments provide atimely and intuitive way for a human medical practitioner to moreaccurately predict OS in GBM, to more accurately classify an ROI or apatient associated with the ROI into a OS survival category (e.g., LTS,STS), thus improving on existing approaches to predicting GBM survival.By displaying the classification and optionally displaying one or moreof the image, the r-DepTH descriptor, the tumoral 3D gradient-basedtextural descriptor, the peritumoral 3D gradient-based texturaldescriptor, or the probability, example embodiments may further providea timely and intuitive way for a human medical practitioner to moreaccurately identify GBM patients as likely to experience LTS or STS, andto improve treatment management accordingly. Embodiments may furtherdisplay operating parameters of the machine learning classifier.

FIG. 4 illustrates a set of operations 400 that is similar to operations200 and includes operations 210-270 as described herein, but thatincludes additional operations. Operations 400 includes, at 412,training the machine learning classifier.

FIG. 5 illustrates a diagram showing an example flow of a method or setof operations 500 that facilitates training of a machine learningclassifier to generate a probability that a patient associated with anROI demonstrating GBM will experience LTS, based on the r-DepTHdescriptor acquired from radiographic (e.g., MRI, CT) image(s),according to various embodiments discussed herein. Method or set ofoperations 500 may be employed by various embodiments described herein,including, for example, operations 400, at 412.

Operations 500 may include, at 510, accessing a training dataset ofradiological images of tissue demonstrating GBM. As explained in greaterdetail herein, the training dataset can comprise a plurality ofradiological images of tissue demonstrating GBM comprising a positiveset that is associated with a first classification (e.g., LTS) and anegative set that is associated with a different second classification(e.g., STS).

Operations 500 may also include, at 520, determining, for each image inthe training dataset, values for that image for each of the N (where Nis a positive integer) most distinguishing features, including featuresused in generating the deformation heterogeneity feature descriptor

_(def), the tumoral 3D gradient-based textural descriptor

_(tex) ^(T), and the peritumoral 3D gradient-based textural descriptor

_(tex) ^(P), for distinguishing LTS from STS. The N most distinguishingfeatures can be determined via any of a variety of algorithm or measures(e.g., sequential forward feature selection, RF, t-test, Wilcoxon ranksum, mRMR, etc.). The N most distinguishing radiomic features may beemployed, at 530, in generating an r-DepTH descriptor, (e.g.,

_(depth)=[

_(tex) ^(T),

_(tex) ^(P),

_(def)]) according to various embodiments discussed herein.

The set of operations 500 can further include, at 540, training amachine learning classifier (e.g., SVM (Support Vector Machine), LDA(Linear Discriminant Analysis) classifier, QDA (Quadratic DiscriminantAnalysis classifier), DLDA (Diagonal Line Discriminant Analysis)classifier, RF (Random Forest) classifier, CNN (Convolutional NeuralNetwork) classifier, etc.) based on the training dataset, and, for eachimage in the training dataset, the values of the N radiographic featuresfor that image (e.g., the r-DepTH descriptor), and a known prognosis(e.g., LTS, STS) associated with that image. Based on the trainingdataset, and, for each image in the training dataset, the values of theN radiographic features for that image, and a known prognosis (e.g.,LTS, STS) associated with that image, the classifier can determineclasses for LTS and STS, and probability of LTS or STS for associatedfeature vectors (e.g., r-DepTH descriptor).

The set of operations 500 can optionally include, at 550, testing themachine learning classifier on a test dataset comprising radiologicalimages for which prognoses are known (e.g., in a manner similar to setof operations 200, additionally comprising comparing a generatedprognosis with the known prognosis). In this manner, the ability of themachine learning classifier to correctly classify radiological brainimages as LTS or STS based on the r-DepTH descriptor can be estimated.In one embodiment, an independent dataset is also accessed, theindependent dataset including a plurality of radiographic images oftissue demonstrating GBM, and clinical information (e.g., OS time for apatient) associated with the patients of which the plurality ofradiographic images comprising the independent dataset is acquired.Testing the machine learning classifier may, in this embodiment, furthercomprise testing the machine learning classifier on the independentdataset according to various embodiments discussed herein.

Training the machine learning classifier can also comprise determiningwhich radiomic features are most discriminative in distinguishing LTSfrom STS, and/or determining the optimal combination of parameters usedin the computation of the probability (e.g., which radiomic features toinclude in generating

_(depth)=[

_(tex) ^(T),

_(tex) ^(P),

_(def)], how many features to employ) can best separate a positive classfrom a negative class (e.g., LTS vs. STS). Embodiments may generate areceiver operating characteristic curve (ROC) and calculate anassociated area under the ROC (AUC).

Training the machine learning classifier may include training themachine learning classifier until a threshold level of accuracy isachieved, until a threshold time has been spent training the machinelearning classifier, until a threshold amount of computational resourceshave been expended training the machine learning classifier, or until auser terminates training. Other training termination conditions may beemployed. Training the machine learning classifier may also includedetermining the optimal combination of parameters used in thecomputation of a probability of LTS (e.g., which radiomic features toextract, number of radiomic features to extract, size of annular bandsin normal parenchyma, number of annular bands, size of peritumoralregion) to best separate a positive and negative class. In oneembodiment, the machine learning classifier is trained until at least anAUC=0.83 or an accuracy of at least 81% in distinguishing LTS from STSis achieved.

Returning to FIG. 4 , the set of operations 400 may further include, at490, generating a personalized GBM treatment plan. The personalized GBMtreatment plan may be generated based, at least in part, on theclassification and optionally on one or more of the r-DepTH descriptor,the probability, or the image. The personalized GBM treatment plan maybe generated for the patient of whom the image was acquired based, atleast in part, on the classification and optionally on one or more ofthe r-DepTH descriptor, the probability, or the image. Defining apersonalized GBM treatment plan facilitates delivering a particulartreatment that will be therapeutically active to the patient, whileminimizing negative or adverse effects experienced by the patient. Forexample, the personalized GBM treatment plan may suggest a surgicaltreatment, may define a pharmaceutical agent dosage or schedule and/orother recommendations for GBM management, for a patient, wherein thespecific recommendation can depend on an OS classification (e.g., LTS,STS) associated with the patient. Generating the personalized GBMtreatment plan includes acquiring electronic data, reading from acomputer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

The set of operations 400 can further include, at 492, optionallydisplaying the personalized GBM treatment plan according to embodimentsdescribed herein.

Techniques and aspects of various embodiments are further explainedbelow, in connection with an example embodiment that facilitatesdetermination of OS (e.g., LTS, STS) for a patient demonstrating GBMrepresented in radiological imagery, including MRI or CT imagery.

Example Use Case: Radiographic-Deformation and Textural Heterogeneity(r-DepTH): An Integrated Descriptor for Brain Tumor Prognosis.

An example embodiment included training a machine learning classifier todistinguish LTS GBM survivors from STS GBM survivors, based on examplecases of LTS and STS GBM survival. FIG. 6 is a workflow diagram of anexample methodology or operations 600 according to embodiments describedherein. In this example, an image scene is defined at 610 as I asI=(C,f), where I is a spatial grid C of voxels c∈C, in a 3-dimensionalspace,

³. Each voxel, c E C is associated with an intensity value f(c). I_(T),I_(P), and I_(N) correspond to the intra-tumoral, peri-tumoral, andsurrounding normal parenchyma sub-volumes within every I respectively,such that [I_(T), I_(P), I_(N)]⊂I. In this example, the sub-volume IN isfurther divided into uniformly sized annular sub-volumes I_(N) ^(j),where j is the number of uniformly sized annular bands, such that j∈{1,. . . , k}, where k is a user-defined proximity parameter dependent onthe distance g from the tumor margin.

In this example, a radiographic-deformation and textural heterogeneity(r-DepTH) descriptor is defined and computed. In this example, at 620,deformation heterogeneity descriptors are extracted from within thenormal parenchyma. A healthy brain atlas (I_(Atlas)), is used to measurethe tissue deformation in the normal appearing brain regions of everypatient volume I. In this example, the healthy brain T1w MNI (MontrealNeurological Institute) atlashttps://www.mcgill.ca/bic/software/tools-data-analysis/anatomical-mri/atlases/icbm152-non-linearis employed. Other healthy brain atlases may be employed. An atlas,including the healthy T1w MNI atlas (e.g., I_(Atlas)), is constructed byregistering healthy brain to a common coordinate space and taking avoxel-wise average of the intensities of the voxels. In this example,I_(Atlas) is first non-rigidly aligned to I using a mutualinformation-based similarity measure provided in ANTs (AdvancedNormalization Tools) SyN (Symmetric Normalization) toolbox. The tumormask Î_(mask) is removed from I during registration such that only thespatial intensity differences due to structural deformation caused bymass effect are recovered, when compared to I_(Atlas). Given thereference (I) and floating (I_(Atlas)), the non-rigid alignment can beformulated as: (I, I_(Atlas))=T(I_(Atlas)) where, T(⋅) is the forwardtransformation of the composite (including affine components) voxel-wisedeformation field that maps the displacements of the voxels between thereference and floating volumes. This transformation also propagates theatlas brain mask (Î_(atlas)) to the subject space, therebyskull-stripping the subjects. As ANTs SyN satisfies the conditions of adiffeomorphic registration, an inverse T⁻¹(·) exists, that successfullymaps I to the I_(Atlas) space. This inverse mapping yields the tissuedeformation of I with respect to I_(Atlas), representing thedeformations exerted on every c∈C_(N), due to the tumor mass effect.Considering (c_(x)′, c_(y)′, c_(z)′) as new voxel positions of I whenmapped to I_(Atlas), the displacement vector is given as [δx,δy,δz]where vector (c_(x)′,c_(y)′,c_(z)′)=(c_(x),c_(y),c_(z))+(δx,δy,δz), andthe magnitude of deformation is given by: D(c)=√{square root over((δx)²+(δy)²+(δz)²)}, for every c∈C_(N) ^(j), and j∈{1, . . . , k}.First order statistics (i.e. mean, median, standard deviation, skewness,and kurtosis) are then computed by aggregating D(c) for every c withinevery sub-volume I_(N) ^(j) yielding a feature descriptor

_(def) ^(i) for every annular sub-region C_(N) ^(j), where C_(N)^(j)⊂C_(N), j∈{1, . . . , k}. In one example, first order statistics arecomputed for less than (e.g., for 75%, 90%) every c within everysub-volume I_(N) ^(j).

In this example, at 630, 3D gradient-based descriptors are extractedfrom tumoral and peritumoral regions. In this example, a 3Dgradient-based texture descriptor is employed. This texture descriptorcaptures tumor heterogeneity by computing higher order statistics fromthe gradient orientation changes computed across X, Y, and Z directions.These features have been shown to be successful in tumorcharacterization for a variety of applications in brain, lung and breastcancers. Briefly, for every c∈[C_(P),C_(T)], gradients along the X, Y,and Z directions are computed as,

${{\nabla{f(c)}} = {{\frac{\partial{f(c)}}{\partial X}\hat{i}} + {\frac{\partial{f(c)}}{\partial Y}\hat{j}} + {\frac{\partial{f(c)}}{\partial Z}\hat{k}}}},$where

$\frac{\partial{f(c)}}{\partial q}$is the gradient magnitude along the q axis, q∈{X, Y, Z}. A N×N×N windowcentered around every c∈C is selected to compute the localized gradientfield. We then compute ∂fX(c_(t)), ∂fY(c_(t)), and ∂fZ(c_(t)), for everyc∈[C_(P),C_(T)], t∈{1, 2, . . . , N³}. The vector gradient matrix Fassociated with every c is given by F=[∂fX(c_(t)),∂fY(c_(t)),∂fZ(c_(t))]where [∂fX(c_(t)),∂fY(c_(t)),∂fZ(c_(t))], t∈{1, 2, . . . , N³} is thematrix of gradient vectors in the X, Y, and Z directions for every c_(t)given by a N³×3 matrix. Singular value decomposition of F for a voxelc_(t) yields three dominant principal components ψ_(X)(c_(t)),ψ_(Y)(c_(t)), and ψ_(Z)(c_(t)) in the X-, Y-, and Z-directionsrespectively. Two principal orientations θ(c_(t)) and ϕ(c_(t)) can thenbe obtained to capture variability in orientations across (X, Y), and(X, Y, Z) (in-plane and out-of-plane variability), given by

${\theta\left( c_{t} \right)} = {{\tan^{- 1}\frac{\psi_{Y}\left( c_{t} \right)}{\psi_{X}\left( c_{t} \right)}\mspace{14mu}{and}\mspace{14mu}\left( c_{t} \right)} = {\tan^{- 1}{\frac{\psi_{Z}\left( c_{t} \right)}{\sqrt{{\psi_{Y}^{2}\left( c_{t} \right)} + {\psi_{X}^{2}\left( c_{t} \right)}}}.}}}$Two separate N×N co-occurrence matrices, M^(θ) and M^(ϕ) are computed,corresponding to θ(c_(t)) and ϕ(c_(t)) which capture the orientationpairs between voxels in a local neighborhood. We then individuallycompute 13 Haralick statistics as [S_(θ) _(b) ,S_(ϕ) _(b) ], b∈[1, 13]from M^(θ) and M^(ϕ), for every voxel c∈[C_(P),C_(T)]. For every b,first order statistics (i.e. mean, median, standard deviation, skewness,and kurtosis) are then computed by aggregating [S_(θ) _(b) ,S_(ϕ) _(b) ]for every c∈[C_(P),C_(T)] yielding a feature descriptor

_(tex) ^(T) for the tumor volume, and

_(tex) ^(P) for the peri-tumoral volume.

In this example, at 640, the descriptor IF depth

_(depth) is obtained as a feature vector

_(depth)=[

_(tex) ^(T),

_(tex) ^(P),

_(def)] by concatenations of the deformation descriptor

_(def), and the texture descriptors

_(tex) ^(T), and

_(tex) ^(P).

In one example, a total of 105 3-Tesla treatment-naive Gadolinium(Gd)-contrast T1w, T2w, and FLAIR MRI GBM studies were retrospectivelyobtained from the Cancer Imaging Archive. In this example, inclusioncriteria were restricted to include short-term survivors with an overallsurvival (OS) of <240 days and long-term survivors with OS>540 days.This resulted in a total of 68 patients in the training cohort, with anequal split of 34 STS and LTS cases respectively. An independent cohortof a total of 11 studies (4 LTS and 7 STS cases), with the same MRIsequences as the training set, was obtained from the collaboratinginstitution. The T1w images were first bias-corrected using N4 biascorrection. The lesion masks were manually delineated by an expertradiologist as tumor, peri-tumoral, and normal parenchymal regions onT1w MRI scans.

In this example, the normal parenchymal region was divided into k=12annular bands, such that neighboring bands were equidistant to eachother at 5 mm. Hence, each brain MRI volume I is associated with a 60×1deformation feature vector

_(def), with a total of 5 statistics (mean, median, standard deviation,skewness, and kurtosis) obtained from each k, k∈[1, . . . , 12].Similarly for

_(tex) ^(T) and

_(tex) ^(P) respectively, the same 5 statistics are computed from [S_(θ)_(b) ,S_(ϕ) _(b) ], |S_(θ) _(b) |=|S_(ϕ) _(b) |=13, resulting in a 130×1feature vector, each. Following feature extraction, sequential forwardfeature selection was employed to identify the most discriminatingsubset of features between STS and LTS from the training cohort. A totalof 50 iterations of three-fold (one fold held-out for testing),patient-stratified, cross-validation scheme was used for constructing alinear discriminant analysis (LDA) classifier using the training set.The top 5 best performing features were obtained for each of the fourfeature sets,

_(def),

_(tex) ^(P),

_(tex) ^(T), and

_(depth) depth, using the training cohort. Additionally, a total of 6shape features (

_(shape)) were also extracted for every I for comparison with the other4 feature sets. The top performing features from each of the 5 featuresets (

_(def),

_(tex) ^(P),

_(tex) ^(T),

_(depth),

_(shape)) were used to lock down five different LDA classifiers, whichwere independently evaluated on the N=11 test cases. Kaplan-Meier (KM)survival analysis, along with log-rank test, was independently employedfor each of the 5 feature sets, to compare survival times between thetwo groups (STS versus LTS). KM curves for this example are illustratedat 650, which represent differences in survival characteristics betweentwo groups of patients (e.g., LTS, STS). The horizontal axis on the KMcurve shows the time in days from initial diagnosis, and the verticalaxis shows the probability of survival. Any point on the curve reflectsthe probability that a patient in each group would remain alive at thatinstance. Labels assigned by the LDA classifier were used for KM-curvegeneration. FIG. 9 illustrates a table 900 that lists features computedfrom T1w MRI scans to distinguish LTS from STS according to variousembodiments described herein.

Embodiments described herein facilitate improved distinguishing of LTSfrom STS compared to existing approaches. In this example, analysis onthe training dataset on

_(def) demonstrated that the skewness of deformation magnitude acrossLTS (FIG. 7 , element 710) and STS (FIG. 7 , element 720) wasconsistently statistically significantly different (p_0.05) for annularregions g>30 millimeters proximal to the tumor (FIG. 7 , elements 712,722). However, the significance did not hold for g>30 millimeters acrossLTS and STS studies. Higher values of skewness are shown in red whilelower values are shown in dark blue in FIG. 7 . Deformation magnitudeswere found to be highly positively skewed (shown in red) in STS ascompared to LTS (FIG. 7 , element 730) (shown in green). Box plots ofdeformation skewness across four different annular bands (e.g., 713)g<=5, 5<g<=10, 30<g<=35, 35<g<=40 (in mm) are illustrated at 740.Results of this example corroborate with recent findings, suggestingthat there may be prognostic impact due to tumor burden in certaincognitive areas because of the structural deformation heterogeneity,eventually affecting survival. Furthermore, the top 5 features on thetraining set (N=68) across

_(def),

_(tex) and

_(depth), yielded an AUC of 0.71+−0.08, 0.77+−0.08 and 0.83+−0.07respectively via a 3-fold cross-validation.

In this example, FIG. 8 , at 810 shows the ideal “ground truth” KM curvefor STS and LTS patients obtained on an independent cohort of (N=11)studies. FIG. 8 , elements 820, 830, and 840 show the KM curves obtainedusing the assigned labels from the LDA classifier using

_(def),

_(tex) and

_(depth) respectively. KM curves using

_(def) (p=0.176),

_(tex) (p=0.81),

_(shape) (p=0.1) alone to distinguish LTS from STS patients, were notfound to be significant. However, the

_(depth) depth descriptor, yielded a statistically significant survivalcurve for distinguishing STS versus LTS with p=0.038. Additionally, theclassifier trained on

_(depth) depth according to embodiments could correctly predict thesurvival group in 9 out of the 11 studies (accuracy=81%), while

_(tex) ^(T) achieved an accuracy of 64%, and

_(tex) ^(P) of 54% in predicting the survival group.

As demonstrated by the example embodiments, various embodiments canfacilitate prediction of OS, including LTS or STS, based on aradiographic-deformation and textural heterogeneity (r-DepTH) descriptorcomputed from radiomic and deformation features extracted fromradiographic images, including MRI or CT, of tissue demonstrating GBM.The ability to more accurately predict OS, including LTS or STS based onthe r-Depth descriptor generated according to various embodimentsdescribed herein and using a machine learning classifier trainedaccording to embodiments described herein can provide the technicalimprovement of increasing the accuracy with which patients areclassified as likely to experience LTS or likely to experience STS.Embodiments thus provide a measurable improvement over existing methods,systems, apparatus, or other devices in reliably and accuratelypredicting patient outcome and improving treatment management in GBM.

In various example embodiments, method(s) discussed herein can beimplemented as computer executable instructions. Thus, in variousembodiments, a computer-readable storage device can store computerexecutable instructions that, when executed by a machine (e.g.,computer, processor), cause the machine to perform methods or operationsdescribed or claimed herein including operation(s) described inconnection with methods or operations 200, 400, or 500, or any othermethods or operations described herein. While executable instructionsassociated with the listed methods or operations are described as beingstored on a computer-readable storage device, it is to be appreciatedthat executable instructions associated with other example methods oroperations described or claimed herein can also be stored on acomputer-readable storage device. In different embodiments, the examplemethods or operations described herein can be triggered in differentways. In one embodiment, a method or operation can be triggered manuallyby a user. In another example, a method or operation can be triggeredautomatically.

Embodiments discussed herein related to distinguishing LTS from STS inGBM are based on features that are not perceivable by the human eye, andtheir computation cannot be practically performed in the human mind. Amachine learning classifier as described herein cannot be implemented inthe human mind or with pencil and paper. Embodiments thus performactions, steps, processes, or other actions that are not practicallyperformed in the human mind, at least because they require a processoror circuitry to access digitized images stored in a computer memory andto extract or compute features that are based on the digitized imagesand not on properties of tissue or the images that are perceivable bythe human eye. Embodiments described herein can use a combined order ofspecific rules, elements, operations, or components that renderinformation into a specific format that can then used and applied tocreate desired results more accurately, more consistently, and withgreater reliability than existing approaches, thereby producing thetechnical effect of improving the performance of the machine, computer,or system with which embodiments are implemented.

FIG. 10 illustrates an example apparatus 1000 that can facilitatedistinguishing LTS from STS in GBM based on radiographic imagery (e.g.,MRI, CT), according to various embodiments discussed herein. Apparatus1000 may be configured to perform various techniques, operations, ormethods discussed herein, for example, training a machine learningclassifier (e.g., LDA classifier, logistic regression model classifier,quadratic discriminant analysis classifier, support vector machine,etc.) based on training data to distinguish LTS from STS in GBM, oremploying such a trained machine learning classifier to generate aclassification of a patient based on an r-DepTH descriptor generatedfrom radiographic imagery. In one embodiment, apparatus 1000 includes aprocessor 1010, and a memory 1020. Processor 1010 may, in variousembodiments, include circuitry such as, but not limited to, one or moresingle-core or multi-core processors. Processor 1010 may include anycombination of general-purpose processors and dedicated processors(e.g., graphics processors, application processors, etc.). Theprocessor(s) can be coupled with and/or can comprise memory (e.g.,memory 1020) or storage and can be configured to execute instructionsstored in the memory 1020 or storage to enable various apparatus,applications, or operating systems to perform operations and/or methodsdiscussed herein.

Memory 1020 is configured to store a radiographic (e.g., MRI, CT) imageassociated with a patient, where the image includes a region of interest(ROI) demonstrating GBM. The radiographic image has a plurality ofpixels, a pixel having an intensity. The image includes a tumoralregion. In some embodiments, memory 1020 can store a training set ofimages (e.g., comprising radiographic images showing radiomic featuresor deformation features, along with a known prognosis, or outcome) fortraining a classifier (e.g., LDA classifier, etc.) to determine aprobability of LTS or STS, while in the same or other embodiments,memory 1020 can store a radiographic image of a patient for whom aprediction of LTS or outcome is to be determined. Memory 1020 can befurther configured to store one or more clinical features or other dataassociated with the patient of the radiographic image. The radiographicimage may have a plurality of voxels, a voxel having an intensity.

Apparatus 1000 also includes an input/output (I/O) interface 1030; a setof circuits 1050; and an interface 1040 that connects the processor1010, the memory 1020, the I/O interface 1030, and the set of circuits1050. I/O interface 1030 may be configured to transfer data betweenmemory 1020, processor 1010, circuits 1050, and external devices, forexample, a medical imaging device such as an MRI system or apparatus.

The set of circuits 1050 includes an image acquisition circuit 1051, aregion definition circuit 1053, a radiographic-deformation and texturalheterogeneity (r-DepTH) descriptor circuit 1055, a GBM OS predictioncircuit 1057, and display circuit 1059.

Image acquisition circuit 1051 is configured to access the MRI image.Accessing the MRI image may include accessing the MRI image stored inmemory 1020. In another embodiment accessing the MRI image may includeacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind. In one embodiment,the MRI image is a 3-Tesla (3 T) treatment-naïve Gadolinium(Gd)-contrast T1w image, a 3 T treatment-naïve T2w Gd-contrast image, ora FLAIR MRI image.

Region definition circuit 1053 is configured to: segment the tumoralregion represented in the MRI image. Segmenting the tumoral regionincludes defining a tumoral boundary. In one embodiment, regiondefinition circuit 1053 is configured to automatically segment thetumoral region using a watershed segmentation technique, a regiongrowing or active contour technique, or a convolutional neural network(CNN) approach. In another embodiment, region definition circuit 1053may be configured to employ other, different segmentation techniques oralgorithms.

Region definition circuit 1053 is also configured to define aperitumoral region represented in the MRI image based on the tumoralregion. Region definition circuit 1053 may be configured to define theperitumoral region by performing a dilation of the tumoral boundary. Theperitumoral region may include a plurality of annular rings. In oneembodiment, performing a dilation of the tumoral boundary includesdilating the tumoral boundary 65 mm. In another embodiment, performing adilation of the tumoral boundary includes dilating the tumoral boundaryanother, different amount (e.g., 9 mm, 15 mm, 50 mm).

Region definition circuit 1053 is also configured to define aparenchymal region represented in the MRI image. The parenchymal regionincludes a plurality of annular sub-regions.

R-DepTH descriptor circuit 1055 is configured to compute a deformationheterogeneity feature descriptor based on the parenchymal region. In oneembodiment, r-DepTH descriptor circuit 1055 is configured to generatethe deformation heterogeneity feature descriptor by accessing a healthybrain atlas; registering the parenchymal region to the healthy brainatlas using a non-rigid mutual information based similarity measureregistration approach; and computing the deformation heterogeneityfeature descriptor based on the registration of the parenchymal regionwith the healthy brain atlas. In another embodiment, r-DepTH descriptorcircuit 1055 may be configured to employ other non-rigid registrationtechniques. In one embodiment, r-DepTH descriptor circuit 1055 isconfigured to compute the deformation heterogeneity feature descriptorbased on first order statistics computed from a deformation magnitude ofeach voxel of each of the plurality of annular sub-regions,respectively.

R-DepTH descriptor circuit 1055 is also configured to compute a tumoralthree-dimensional (3D) gradient-based texture descriptor based on thesegmented tumoral region. R-DepTH descriptor circuit 1055 is alsoconfigured to compute a peritumoral 3D gradient-based texturaldescriptor based on the peritumoral region. In one embodiment, thetumoral 3D gradient-based texture descriptor includes a co-occurrence oflocal anisotropic gradient orientations (CoLlaGe) feature, and theperitumoral 3D gradient-based textural descriptor includes a CoLlaGefeature. In one embodiment, the tumoral 3D gradient-based texturedescriptor includes five first order statistics of entropy, energy,inertia, IDM, correlation, Info1, Info2, sum average, sum variance, sumentropy, difference average, difference variance, and differentialentropy, respectively. In this embodiment, the peritumoral 3Dgradient-based textural descriptor includes a CoLlaGe feature. Inanother embodiment, the tumoral 3D gradient-based texture descriptor orthe peritumoral 3D gradient-based texture descriptor may include other,different features or numbers of features.

R-DepTH descriptor circuit 1055 is further configured to generate anr-DepTH descriptor based on the deformation heterogeneity featuredescriptor, the tumoral 3D gradient-based textural descriptor, and theperitumoral 3D gradient-based textural descriptor. In one embodiment,r-DepTH descriptor circuit 1055 is configured to generate the r-DepTHdescriptor as a feature vector

_(depth)=[

_(tex) ^(T),

_(tex) ^(P),

_(def)] by concatenations of the deformation descriptor

_(def), and the texture descriptors

_(tex) ^(T) and

_(tex) ^(P) according to various embodiments described herein.

GBM OS prediction circuit 1057 is configured to compute a probabilitythat the patient associated with the ROI will experience LTS. GBM OSprediction circuit 1057 is configured to compute the probability basedon the r-DepTH descriptor. GBM OS prediction circuit 1057 is alsoconfigured to generate a classification of the patient as likely toexperience LTS or as likely to experience STS based, at least in part,on the probability. In one embodiment, GBM OS prediction circuit 1057 isconfigured as a linear discriminant analysis (LDA) machine learningclassifier. In another embodiment, GBM OS prediction circuit 1057 isconfigured as another, different type of machine learning classifierincluding, for example, a QDA classifier, an SVM classifier, a randomforest classifier, or a CNN classifier.

Display circuit 1059 is configured to display the classification. Invarious embodiments, the classification may include one or more of amost likely outcome (e.g., as determined based on the r-DepTHdescriptor) such membership in a first class or second, different class(e.g., LTS, STS), a probability or confidence associated with a mostlikely outcome; and/or associated probabilities/confidences associatedwith each of a plurality of outcomes. Display circuit 1059 may befurther configured to optionally display the image, the probability, ther-DepTH descriptor, or other data associated with the operation ofapparatus 1000.

FIG. 11 illustrates an apparatus 1100 that is similar to apparatus 1000but that includes additional elements and details. In one embodiment ofapparatus 1100, the set of circuits 1050 further includes a GBMpersonalized treatment plan circuit 1153. GBM personalized treatmentplan circuit 1153 is configured to generate a personalized GBM treatmentplan based, at least in part, on the classification. GBM personalizedtreatment plan circuit 1153 may be configured to generate a personalizedtreatment plan based, at least in part, on a classification obtainedfrom GBM OS prediction circuit 1057 or display circuit 1059. GBMpersonalized treatment plan circuit 1153 may be configured to generate apersonalized treatment plan for the patient of whom the image wasacquired based, at least in part, on the classification derivedtherefrom. Defining a personalized treatment plan facilitates deliveringa particular treatment that will be therapeutically active to thepatient, while minimizing negative or adverse effects experienced by thepatient. For example, the personalized treatment plan may suggest asurgical treatment, may suggest a pharmaceutical agent dosage orschedule, and/or other treatments. Generating a personalized treatmentplan based on a more accurate prediction of OS in GBM or a more accurateprediction of LTS or STS facilitates more efficient delivery of costlytherapeutic or surgical treatments to patients more likely to benefitfrom such treatments. For example, the personalized treatment plan maysuggest a first surgical treatment, may suggest a first pharmaceuticalagent dosage or schedule, and/or other treatments for a patientclassified as likely to experience LTS, or may suggest a second,different surgical treatment or second, different pharmaceutical agentdosage or schedule or treatments for a patient classified as likely toexperience STS. In this embodiment, display circuit 1059 is furtherconfigured to optionally display the personalized treatment plan.

In one embodiment of apparatus 1100, the set of circuits 1050 furtherincludes a training and testing circuit 1155. Training and testingcircuit 1155 is configured to train GBM OS prediction circuit 1057 on atraining cohort; and optionally test GBM OS prediction circuit 1057 on atesting cohort, according to various embodiments described herein.

In one embodiment, apparatus 1100 further includes personalized medicinedevice 1160. Apparatus 1100 may be configured to provide theprobability, the classification, a personalized treatment plan, or otherdata to personalized medicine device 1160. Personalized medicine device1160 may be, for example, a computer assisted diagnosis (CADx) system orother type of personalized medicine device that can be used tofacilitate the prediction of OS in GBM. In one embodiment, GBMpersonalized treatment plan circuit 1153 can control personalizedmedicine device 1160 to display the probability, the classification, apersonalized treatment plan, or other data to on a computer monitor, asmartphone display, a tablet display, or other displays.

FIG. 12 illustrates an example computer 1200 in which example methodsillustrated herein can operate and in which example methods, apparatus,circuits, operations, or logics may be implemented. In differentexamples, computer 1200 may be part of a GBM OS prediction system orapparatus, a GBM tumor classification system or apparatus, a CADxsystem, an MRI system, a CT system, a digital whole slide scanner, or apersonalized medicine system, or may be operably connectable to a GBM OSprediction system or apparatus, a GBM tumor classification system orapparatus, a CADx system, an MRI system, a CT system, a digital wholeslide scanner, or a personalized medicine system.

Computer 1200 includes a processor 1202, a memory 1204, and input/output(I/O) ports 1210 operably connected by a bus 1208. In one example,computer 1200 may include a set of logics or circuits 1230 that performoperations for or a method of predicting OS in GBM, or classifying GBMtumors on MRI imagery, including by using a machine learning classifier.Thus, the set of circuits 1230, whether implemented in computer 1200 ashardware, firmware, software, and/or a combination thereof may providemeans (e.g., hardware, firmware, circuits) for predicting OS in GBM, orclassifying GBM tumors on radiographic imagery, including MRI or CTimagery. In different examples, the set of circuits 1230 may bepermanently and/or removably attached to computer 1200.

Processor 1202 can be a variety of various processors including dualmicroprocessor and other multi-processor architectures. Processor 1202may be configured to perform steps of methods claimed and describedherein. Memory 1204 can include volatile memory and/or non-volatilememory. A disk 1206 may be operably connected to computer 1200 via, forexample, an input/output interface (e.g., card, device) 1218 and aninput/output port 1210. Disk 1206 may include, but is not limited to,devices like a magnetic disk drive, a tape drive, a Zip drive, a flashmemory card, or a memory stick. Furthermore, disk 1206 may includeoptical drives like a CD-ROM or a digital video ROM drive (DVD ROM).Memory 1204 can store processes 1214 or data 1217, for example. Data1217 may, in one embodiment, include digitized radiological images,including MRI images of tissue demonstrating GBM. Disk 1206 or memory1204 can store an operating system that controls and allocates resourcesof computer 1200.

Bus 1208 can be a single internal bus interconnect architecture or otherbus or mesh architectures. While a single bus is illustrated, it is tobe appreciated that computer 1200 may communicate with various devices,circuits, logics, and peripherals using other buses that are notillustrated (e.g., PCIE, SATA, Infiniband, 1394, USB, Ethernet).

Computer 1200 may interact with input/output devices via I/O interfaces1218 and input/output ports 1210. Input/output devices can include, butare not limited to, MRI systems, CT systems, digital whole slidescanners, an optical microscope, a keyboard, a microphone, a pointingand selection device, cameras, video cards, displays, disk 1206, networkdevices 1220, or other devices. Input/output ports 1210 can include butare not limited to, serial ports, parallel ports, or USB ports.

Computer 1200 may operate in a network environment and thus may beconnected to network devices 1220 via I/O interfaces 1218 or I/O ports1210. Through the network devices 1220, computer 1200 may interact witha network. Through the network, computer 1200 may be logically connectedto remote computers. The networks with which computer 1200 may interactinclude, but are not limited to, a local area network (LAN), a wide areanetwork (WAN), or other networks, including the cloud.

Examples herein can include subject matter such as an apparatus, an MRIsystem, a CT system, an optical microscopy system, a personalizedmedicine system, a CADx system, a processor, a system, circuitry, amethod, means for performing acts, steps, or blocks of the method, atleast one machine-readable medium including executable instructionsthat, when performed by a machine (e.g., a processor with memory, anapplication-specific integrated circuit (ASIC), a field programmablegate array (FPGA), or the like) cause the machine to perform acts of themethod or of an apparatus or system for predicting OS in GBM, accordingto embodiments and examples described.

Example 1 is a non-transitory computer-readable storage device storingcomputer-executable instructions that when executed cause a processor toperform operations, the operations comprising: accessing a radiologicalimage of a region of interest (ROI) demonstrating Glioblastoma (GBM),where the ROI includes a tumoral region, where the image is associatedwith a patient, where the image has a plurality of voxels, a voxelhaving an intensity; segmenting the tumoral region represented in theimage; defining a peritumoral region represented in the image based onthe tumoral region; defining a parenchymal region represented in theimage; computing a deformation heterogeneity feature descriptor based onthe parenchymal region; computing a tumoral three-dimensional (3D)gradient-based texture descriptor based on the segmented tumoral region;computing a peritumoral 3D gradient-based textural descriptor based onthe peritumoral region; generating a radiographic-deformation andtextural heterogeneity (r-DepTH) descriptor based on the deformationheterogeneity feature descriptor, the tumoral 3D gradient-based texturaldescriptor, and the peritumoral 3D gradient-based textural descriptor;providing the r-DepTH descriptor to a machine learning classifiertrained to distinguish long-term survival (LTS) from short-term survival(STS) in GBM based on the r-DepTH descriptor; receiving, from themachine learning classifier, a probability that the patient willexperience LTS, where the machine learning classifier computes theprobability based on the r-Depth descriptor; generate a classificationof the patient as likely to experience LTS or as likely to experienceSTS based, at least in part, on the probability; and displaying theclassification.

Example 2 comprises the subject matter of any variation of any ofexample(s) 1, where the radiological image is a magnetic resonanceimaging (MRI) image.

Example 3 comprises the subject matter of any variation of any ofexample(s) 1-2, where the radiological image is a 3-Tesla (3 T)treatment-naïve Gadolinium (Gd)-contrast T1w image, a 3 Ttreatment-naïve T2w Gd-contrast image, or a FLAIR MRI image.

Example 4 comprises the subject matter of any variation of any ofexample(s) 1-3, where computing the deformation heterogeneity featuredescriptor comprises: accessing a healthy brain atlas; registering theparenchymal region to the healthy brain atlas; and computing thedeformation heterogeneity feature descriptor based on the registrationof the parenchymal region with the healthy brain atlas.

Example 5 comprises the subject matter of any variation of any ofexample(s) 1-4, where registering the parenchymal region to the healthybrain atlas includes registering the parenchymal region to the healthybrain atlas using a non-rigid mutual information based similaritymeasure registration approach.

Example 6 comprises the subject matter of any variation of any ofexample(s) 1-5, where the parenchymal region includes a plurality ofannular sub-regions.

Example 7 comprises the subject matter of any variation of any ofexample(s) 1-6, where each member of the plurality of annularsub-regions is a 5 mm annular sub-region.

Example 8 comprises the subject matter of any variation of any ofexample(s) 1-7, where the deformation heterogeneity feature descriptoris computed based on first order statistics computed from a deformationmagnitude of each voxel of each of the plurality of annular sub-regions,respectively.

Example 9 comprises the subject matter of any variation of any ofexample(s) 1-8, where the tumoral 3D gradient-based texture descriptorincludes a co-occurrence of local anisotropic gradient orientations(CoLlaGe) feature.

Example 10 comprises the subject matter of any variation of any ofexample(s) 1-9, where the tumoral 3D gradient-based texture descriptorincludes five first order statistics of entropy, energy, inertia, IDM,correlation, Info1, Info2, sum average, sum variance, sum entropy,difference average, difference variance, and differential entropy,respectively.

Example 11 comprises the subject matter of any variation of any ofexample(s) 1-10, where the peritumoral 3D gradient-based texturaldescriptor includes a co-occurrence of local anisotropic gradientorientations (CoLlaGe) feature.

Example 12 comprises the subject matter of any variation of any ofexample(s) 1-11, where the peritumoral 3D gradient-based texturedescriptor includes five first order statistics of entropy, energy,inertia, IDM, correlation, Info1, Info2, sum average, sum variance, sumentropy, difference average, difference variance, and differentialentropy, respectively.

Example 13 comprises the subject matter of any variation of any ofexample(s) 1-12, where the machine learning classifier is a lineardiscriminant analysis (LDA) classifier.

Example 14 comprises the subject matter of any variation of any ofexample(s) 1-13, the operations further comprising training the machinelearning classifier and optionally testing the machine learningclassifier.

Example 15 comprises the subject matter of any variation of any ofexample(s) 1-14, the operations further comprising generating apersonalized GBM treatment plan based, at least in part, on theclassification, and optionally displaying the personalized GBM treatmentplan.

Example 16 comprises an apparatus comprising: a processor; a memoryconfigured to store a magnetic resonance imaging (MRI) image of a regionof interest (ROI) demonstrating Glioblastoma (GBM), where the ROIincludes a tumoral region, where the image is associated with a patient,where the image has a plurality of voxels, a voxel having an intensity;an input/output (I/O) interface; a set of circuits; and an interfacethat connects the processor, the memory, the I/O interface, and the setof circuits, the set of circuits comprising: an image acquisitioncircuit configured to: access the MRI image; a region definition circuitconfigured to: segment the tumoral region represented in the MRI image;define a peritumoral region represented in the MRI image based on thetumoral region; define a parenchymal region represented in the MRIimage, where the parenchymal region includes a plurality of annularsub-regions; a radiographic-deformation and textural heterogeneity(r-DepTH) descriptor circuit configured to: compute a deformationheterogeneity feature descriptor based on the parenchymal region;compute a tumoral three-dimensional (3D) gradient-based texturedescriptor based on the segmented tumoral region; compute a peritumoral3D gradient-based textural descriptor based on the peritumoral region;and generate an r-DepTH descriptor based on the deformationheterogeneity feature descriptor, the tumoral 3D gradient-based texturaldescriptor, and the peritumoral 3D gradient-based textural descriptor; aGBM overall survival (OS) prediction circuit configured to: compute aprobability that the patient will experience LTS in GBM based on ther-DepTH descriptor; and generate a classification of the patient aslikely to experience LTS or as likely to experience STS based, at leastin part, on the probability; and a display circuit configured to displaythe classification and to optionally display the probability, ther-DepTH descriptor, or the MRI image.

Example 17 comprises the subject matter of any variation of any ofexample 16, where the tumoral 3D gradient-based texture descriptorincludes a co-occurrence of local anisotropic gradient orientations(CoLlaGe) feature; and where the peritumoral 3D gradient-based texturaldescriptor includes a CoLlaGe feature.

Example 18 comprises the subject matter of any variation of any ofexample(s) 16-17, where the r-DepTH descriptor circuit is configured togenerate the deformation heterogeneity feature descriptor by: accessinga healthy brain atlas; registering the parenchymal region to the healthybrain atlas using a non-rigid mutual information based similaritymeasure registration approach; and computing the deformationheterogeneity feature descriptor based on the registration of theparenchymal region with the healthy brain atlas.

Example 19 comprises the subject matter of any variation of any ofexample(s) 16-18, where the r-DepTH descriptor circuit is configured tocompute the deformation heterogeneity feature descriptor based on firstorder statistics computed from a deformation magnitude of each voxel ofeach of the plurality of annular sub-regions, respectively.

Example 20 comprises a non-transitory computer-readable storage devicestoring computer-executable instructions that when executed cause aprocessor to perform operations, the operations comprising: accessing amagnetic resonance imaging (MRI) image of a region of interest (ROI)demonstrating Glioblastoma (GBM), where the ROI includes a tumoralregion, a peritumoral region, and a parenchymal region, where theparenchymal region comprises a plurality of annular sub-regions, wherethe image is associated with a patient, where the image has a pluralityof voxels, a voxel having an intensity, where the MRI image is a 3-Tesla(3 T) treatment-naïve Gadolinium (Gd)-contrast T1w image, a 3 Ttreatment-naïve T2w Gd-contrast image, or a FLAIR MRI image; computing adeformation heterogeneity feature descriptor based on the parenchymalregion by: accessing a healthy brain atlas; registering the parenchymalregion to the healthy brain atlas using a non-rigid mutual informationbased similarity measure registration approach; and computing thedeformation heterogeneity feature descriptor based on the registrationof the parenchymal region with the healthy brain atlas; computing atumoral three-dimensional (3D) gradient-based texture descriptor basedon the tumoral region, where the tumoral 3D gradient-based texturedescriptor includes a co-occurrence of local anisotropic gradientorientations (CoLlaGe) feature; computing a peritumoral 3Dgradient-based textural descriptor based on the peritumoral region,where the peritumoral 3D gradient-based textural descriptor includes aCoLlaGe feature; generating a radiographic-deformation and texturalheterogeneity (r-DepTH) descriptor based on the deformationheterogeneity feature descriptor, the tumoral 3D gradient-based texturaldescriptor, and the peritumoral 3D gradient-based textural descriptor;providing the r-DepTH descriptor to a linear discriminant analysis (LDA)classifier trained to distinguish long-term survival (LTS) fromshort-term survival (STS) in GBM based on the r-DepTH descriptor;receiving, from the LDA classifier, a probability that the patient willexperience LTS, where the LDA classifier computes the probability basedon the r-Depth descriptor; generating a classification of the patient aslikely to experience LTS or as likely to experience STS based, at leastin part, on the probability; and displaying the classification, andoptionally displaying the probability, the r-Depth descriptor, or theMRI image.

Example 21 comprises a machine readable storage device that storesinstructions for execution by a processor to perform any of thedescribed operations of examples 1-20.

Example 22 comprises an apparatus comprising: a memory; and one or moreprocessors configured to: perform any of the described operations ofexamples 1-20.

References to “one embodiment”, “an embodiment”, “one example”, and “anexample” indicate that the embodiment(s) or example(s) so described mayinclude a particular feature, structure, characteristic, property,element, or limitation, but that not every embodiment or examplenecessarily includes that particular feature, structure, characteristic,property, element or limitation. Furthermore, repeated use of the phrase“in one embodiment” does not necessarily refer to the same embodiment,though it may.

“Computer-readable storage device”, as used herein, refers to a devicethat stores instructions or data. “Computer-readable storage device”does not refer to propagated signals. A computer-readable storage devicemay take forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, tapes, and other media. Volatile media mayinclude, for example, semiconductor memories, dynamic memory, and othermedia. Common forms of a computer-readable storage device may include,but are not limited to, a floppy disk, a flexible disk, a hard disk, amagnetic tape, other magnetic medium, an application specific integratedcircuit (ASIC), a compact disk (CD), other optical medium, a randomaccess memory (RAM), a read only memory (ROM), a memory chip or card, amemory stick, and other media from which a computer, a processor orother electronic device can read.

“Circuit”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, or combinations of each toperform a function(s) or an action(s), or to cause a function or actionfrom another logic, method, or system. A circuit may include a softwarecontrolled microprocessor, a discrete logic (e.g., ASIC), an analogcircuit, a digital circuit, a programmed logic device, a memory devicecontaining instructions, and other physical devices. A circuit mayinclude one or more gates, combinations of gates, or other circuitcomponents. Where multiple logical circuits are described, it may bepossible to incorporate the multiple logical circuits into one physicalcircuit. Similarly, where a single logical circuit is described, it maybe possible to distribute that single logical circuit between multiplephysical circuits.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

Throughout this specification and the claims that follow, unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood to beterms of inclusion and not exclusion. For example, when such terms areused to refer to a stated integer or group of integers, such terms donot imply the exclusion of any other integer or group of integers.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

While example systems, methods, and other embodiments have beenillustrated by describing examples, and while the examples have beendescribed in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the systems, methods, and other embodiments described herein.Therefore, the invention is not limited to the specific details, therepresentative apparatus, and illustrative examples shown and described.Thus, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims.

What is claimed is:
 1. A non-transitory computer-readable storage devicestoring computer-executable instructions that when executed cause aprocessor to perform operations, the operations comprising: accessing aradiological image of a region of interest (ROI) demonstratingGlioblastoma (GBM), where the ROI includes a tumoral region, where theradiological image is associated with a patient, where the radiologicalimage has a plurality of voxels, a voxel having an intensity; segmentingthe tumoral region represented in the radiological image; defining aperitumoral region represented in the radiological image based on thetumoral region; defining a parenchymal region represented in theradiological image; computing a deformation heterogeneity featuredescriptor based on the parenchymal region; computing a tumoralthree-dimensional (3D) gradient-based texture descriptor based on thesegmented tumoral region; computing a peritumoral 3D gradient-basedtextural descriptor based on the peritumoral region; generating aradiographic-deformation and textural heterogeneity (r-DepTH) descriptorbased on the deformation heterogeneity feature descriptor, the tumoral3D gradient-based textural descriptor, and the peritumoral 3Dgradient-based textural descriptor; providing the r-DepTH descriptor toa machine learning classifier trained to distinguish long-term survival(LTS) from short-term survival (STS) in GBM based on the r-DepTHdescriptor; receiving, from the machine learning classifier, aprobability that the patient will experience LTS, where the machinelearning classifier computes the probability based on the r-Depthdescriptor; generating a classification of the patient as likely toexperience LTS or as likely to experience STS based, at least in part,on the probability; and displaying the classification.
 2. Thenon-transitory computer-readable storage device of claim 1, where theradiological image is a magnetic resonance imaging (MRI) image.
 3. Thenon-transitory computer-readable storage device of claim 2, where theradiological image is a 3-Tesla (3T) treatment-naïve Gadolinium(Gd)-contrast T1w image, a 3T treatment-naïve T2w Gd-contrast image, ora FLAIR MRI image.
 4. The non-transitory computer-readable storagedevice of claim 1, where computing the deformation heterogeneity featuredescriptor comprises: accessing a healthy brain atlas; registering theparenchymal region to the healthy brain atlas; and computing thedeformation heterogeneity feature descriptor based on the registrationof the parenchymal region with the healthy brain atlas.
 5. Thenon-transitory computer-readable storage device of claim 4, whereregistering the parenchymal region to the healthy brain atlas includesregistering the parenchymal region to the healthy brain atlas using anon-rigid mutual information based similarity measure registrationapproach.
 6. The non-transitory computer-readable storage device ofclaim 4, where the parenchymal region includes a plurality of annularsub-regions.
 7. The non-transitory computer-readable storage device ofclaim 6, where each member of the plurality of annular sub-regions is a5 mm annular sub-region.
 8. The non-transitory computer-readable storagedevice of claim 6, where the deformation heterogeneity featuredescriptor is computed based on first order statistics computed from adeformation magnitude of each voxel of each of the plurality of annularsub-regions, respectively.
 9. The non-transitory computer-readablestorage device of claim 1, where the tumoral 3D gradient-based texturedescriptor includes a co-occurrence of local anisotropic gradientorientations (CoLlaGe) feature.
 10. The non-transitory computer-readablestorage device of claim 9, where the tumoral 3D gradient-based texturedescriptor includes five first order statistics of entropy, energy,inertia, IDM, correlation, Info1, Info2, sum average, sum variance, sumentropy, difference average, difference variance, and differentialentropy, respectively.
 11. The non-transitory computer-readable storagedevice of claim 1, where the peritumoral 3D gradient-based texturaldescriptor includes a co-occurrence of local anisotropic gradientorientations (CoLlaGe) feature.
 12. The non-transitory computer-readablestorage device of claim 11, where the peritumoral 3D gradient-basedtexture descriptor includes five first order statistics of entropy,energy, inertia, IDM, correlation, Info1, Info2, sum average, sumvariance, sum entropy, difference average, difference variance, anddifferential entropy, respectively.
 13. The non-transitorycomputer-readable storage device of claim 1, where the machine learningclassifier is a linear discriminant analysis (LDA) classifier.
 14. Thenon-transitory computer-readable storage device of claim 1, theoperations further comprising training the machine learning classifierand optionally testing the machine learning classifier.
 15. Thenon-transitory computer-readable storage device of claim 1, theoperations further comprising generating a personalized GBM treatmentplan based, at least in part, on the classification, and optionallydisplaying the personalized GBM treatment plan.
 16. An apparatuscomprising: a processor; a memory configured to store a magneticresonance imaging (MRI) image of a region of interest (ROI)demonstrating Glioblastoma (GBM), where the ROI includes a tumoralregion, where the image is associated with a patient, where the imagehas a plurality of voxels, a voxel having an intensity; an input/output(I/O) interface; a set of circuits; and an interface that connects theprocessor, the memory, the I/O interface, and the set of circuits, theset of circuits comprising: an image acquisition circuit configured to:access the MRI image; a region definition circuit configured to: segmentthe tumoral region represented in the MRI image; define a peritumoralregion represented in the MRI image based on the tumoral region; definea parenchymal region represented in the MRI image, where the parenchymalregion includes a plurality of annular sub-regions; aradiographic-deformation and textural heterogeneity (r-DepTH) descriptorcircuit configured to: compute a deformation heterogeneity featuredescriptor based on the parenchymal region; compute a tumoralthree-dimensional (3D) gradient-based texture descriptor based on thesegmented tumoral region; compute a peritumoral 3D gradient-basedtextural descriptor based on the peritumoral region; and generate anr-DepTH descriptor based on the deformation heterogeneity featuredescriptor, the tumoral 3D gradient-based textural descriptor, and theperitumoral 3D gradient-based textural descriptor; a GBM overallsurvival (OS) prediction circuit configured to: compute a probabilitythat the patient will experience LTS in GBM based on the r-DepTHdescriptor; and generate a classification of the patient as likely toexperience LTS or as likely to experience STS based, at least in part,on the probability; and a display circuit configured to display theclassification and to optionally display the probability, the r-DepTHdescriptor, or the MRI image.
 17. The apparatus of claim 16, where thetumoral 3D gradient-based texture descriptor includes a co-occurrence oflocal anisotropic gradient orientations (CoLlaGe) feature; and where theperitumoral 3D gradient-based textural descriptor includes a CoLlaGefeature.
 18. The apparatus of claim 16, where the r-DepTH descriptorcircuit is configured to generate the deformation heterogeneity featuredescriptor by: accessing a healthy brain atlas; registering theparenchymal region to the healthy brain atlas using a non-rigid mutualinformation based similarity measure registration approach; andcomputing the deformation heterogeneity feature descriptor based on theregistration of the parenchymal region with the healthy brain atlas. 19.The apparatus of claim 18, where the r-DepTH descriptor circuit isconfigured to compute the deformation heterogeneity feature descriptorbased on first order statistics computed from a deformation magnitude ofeach voxel of each of the plurality of annular sub-regions,respectively.
 20. A non-transitory computer-readable storage devicestoring computer-executable instructions that when executed cause aprocessor to perform operations, the operations comprising: accessing amagnetic resonance imaging (MRI) image of a region of interest (ROI)demonstrating Glioblastoma (GBM), where the ROI includes a tumoralregion, a peritumoral region, and a parenchymal region, where theparenchymal region comprises a plurality of annular sub-regions, wherethe image is associated with a patient, where the image has a pluralityof voxels, a voxel having an intensity, where the MRI image is a 3-Tesla(3T) treatment-naïve Gadolinium (Gd)-contrast T1w image, a 3Ttreatment-naïve T2w Gd-contrast image, or a FLAIR MRI image; computing adeformation heterogeneity feature descriptor based on the parenchymalregion by: accessing a healthy brain atlas; registering the parenchymalregion to the healthy brain atlas using a non-rigid mutual informationbased similarity measure registration approach; and computing thedeformation heterogeneity feature descriptor based on the registrationof the parenchymal region with the healthy brain atlas; computing atumoral three-dimensional (3D) gradient-based texture descriptor basedon the tumoral region, where the tumoral 3D gradient-based texturedescriptor includes a co-occurrence of local anisotropic gradientorientations (CoLlaGe) feature; computing a peritumoral 3Dgradient-based textural descriptor based on the peritumoral region,where the peritumoral 3D gradient-based textural descriptor includes aCoLlaGe feature; generating a radiographic-deformation and texturalheterogeneity (r-DepTH) descriptor based on the deformationheterogeneity feature descriptor, the tumoral 3D gradient-based texturaldescriptor, and the peritumoral 3D gradient-based textural descriptor;providing the r-DepTH descriptor to a linear discriminant analysis (LDA)classifier trained to distinguish long-term survival (LTS) fromshort-term survival (STS) in GBM based on the r-DepTH descriptor;receiving, from the LDA classifier, a probability that the patient willexperience LTS, where the LDA classifier computes the probability basedon the r-DepTH descriptor; generating a classification of the patient aslikely to experience LTS or as likely to experience STS based, at leastin part, on the probability; and displaying the classification, andoptionally displaying the probability, the r-DepTH descriptor, or theMRI image.